In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule from large small-molecule drug library is evaluated for physical property such as the docking score against a target receptor. In real-life drug discovery experiments, the drug libraries are extremely large but still a minor representation of the essentially infinite chemical space, and evaluation of physical property for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening ("MEMES") based on Bayesian optimization is proposed for efficient sampling of chemical space. The proposed framework is demonstrated to identify 90% of top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.
Tables of performance of ExactMEMES and DeepMEMES, performance comparison of MEMES with Deep Docking, Figures of structure of top hits, distribution plots of binding affinities, distributions of molecular clusters, distributions of binding affinities of missed hits, fractions matched against sampled percentage, protein-ligand complexes and protein-ligand interactions, and supplementary discussions and methods.